code: def compute_video(lst):
      i, video_id, data, label = lst
      feat = [x for x in data]
      feat = np.mean(feat, axis=0)
      pred = np.argmax(feat)
      top1 = (int(pred) == int(label)) * 1.0
      top5 = (int(label) in np.argsort(-feat)[:5]) * 1.0
      return [pred, top1, top5, int(label)]
  
file_path: gluon-cv/scripts/action-recognition/test_ddp_pytorch.py
imports: import os
  import argparse
  import numpy
  import torch
  import torch.nn
  import torch.distributed
  import torch.optim
  from tensorboardX import SummaryWriter
  from gluoncv.torch.model_zoo import get_model
  from gluoncv.torch.utils.model_utils import deploy_model, load_model
  from gluoncv.torch.data import build_dataloader_test
  from gluoncv.torch.utils.task_utils import test_classification
  from gluoncv.torch.engine.config import get_cfg_defaults
  from gluoncv.torch.engine.launch import spawn_workers
  from gluoncv.torch.utils.utils import build_log_dir
label_desc: Use image node_id 1 for calling a global function with example usage: compute_video(lst) and returns: list